Using TensorFlow backend.
2019-07-19 15:25:48,300 - BBC.3.2.py:111- INFO - Start Training
WARNING:tensorflow:From /home/xiaorong/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-07-19 15:25:51.100936: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2019-07-19 15:25:51.118771: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2199995000 Hz
2019-07-19 15:25:51.122493: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5599fff7e680 executing computations on platform Host. Devices:
2019-07-19 15:25:51.122534: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-07-19 15:25:51.550444: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x5599ffffee60 executing computations on platform CUDA. Devices:
2019-07-19 15:25:51.550493: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-07-19 15:25:51.551057: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.545
pciBusID: 0000:86:00.0
totalMemory: 10.73GiB freeMemory: 10.53GiB
2019-07-19 15:25:51.551084: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-07-19 15:25:51.554066: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-07-19 15:25:51.554091: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-07-19 15:25:51.554101: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-07-19 15:25:51.554601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10248 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:86:00.0, compute capability: 7.5)
WARNING:tensorflow:From /home/xiaorong/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3144: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 1, None, 128)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 128, None, 128)    1280      
_________________________________________________________________
batch_normalization_1 (Batch (None, 128, None, 128)    512       
_________________________________________________________________
activation_1 (Activation)    (None, 128, None, 128)    0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 128, None, 25)     0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 128, None, 25)     0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 128, None, 25)     147584    
_________________________________________________________________
batch_normalization_2 (Batch (None, 128, None, 25)     512       
_________________________________________________________________
activation_2 (Activation)    (None, 128, None, 25)     0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 128, None, 12)     0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 128, None, 12)     0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 128, None, 12)     147584    
_________________________________________________________________
batch_normalization_3 (Batch (None, 128, None, 12)     512       
_________________________________________________________________
activation_3 (Activation)    (None, 128, None, 12)     0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 128, None, 6)      0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 128, None, 6)      0         
_________________________________________________________________
permute_1 (Permute)          (None, None, 128, 6)      0         
_________________________________________________________________
reshape_1 (Reshape)          (None, None, 768)         0         
_________________________________________________________________
bidirectional_1 (Bidirection (None, None, 32)          153792    
_________________________________________________________________
bidirectional_2 (Bidirection (None, None, 32)          12480     
_________________________________________________________________
time_distributed_1 (TimeDist (None, None, 32)          1056      
_________________________________________________________________
dropout_4 (Dropout)          (None, None, 32)          0         
_________________________________________________________________
time_distributed_2 (TimeDist (None, None, 6)           198       
_________________________________________________________________
strong_out (Activation)      (None, None, 6)           0         
=================================================================
Total params: 465,510
Trainable params: 464,742
Non-trainable params: 768
_________________________________________________________________
2019-07-19 15:25:54,381 - BBC.3.2.py:133- INFO - Train Parameters: frames_1_sec: 43, chunk_len: 5
2019-07-19 15:25:54,381 - BBC.3.2.py:135- INFO - Feature Parameters: chunk_size: 256, batch_size: 64, num_epochs: 100
2019-07-19 15:25:54,381 - BBC.3.2.py:137- INFO - Model Parameters: cnn_nb_filt: 128, cnn_pool_size: [5, 2, 2], rnn_nb: [32, 32], fc_nb: [32], dropout_rate: 0.5

WARNING:tensorflow:From /home/xiaorong/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/100
2019-07-19 15:26:02.988702: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
 - 99s - loss: 0.5434 - val_loss: 0.4333
2019-07-19 15:27:53,824 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.0, ER_overall: 1.0, Best ER: inf, best_epoch: 0
Epoch 2/100
 - 83s - loss: 0.4336 - val_loss: 0.3127
2019-07-19 15:29:30,624 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.2114, ER_overall: 0.8785, Best ER: inf, best_epoch: 0
Epoch 3/100
 - 85s - loss: 0.3733 - val_loss: 0.3302
2019-07-19 15:31:08,357 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.233, ER_overall: 0.8627, Best ER: inf, best_epoch: 0
Epoch 4/100
 - 82s - loss: 0.3385 - val_loss: 0.2903
2019-07-19 15:32:43,093 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.5131, ER_overall: 0.6273, Best ER: inf, best_epoch: 0
Epoch 5/100
 - 82s - loss: 0.3209 - val_loss: 0.2575
2019-07-19 15:34:18,603 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6014, ER_overall: 0.5294, Best ER: inf, best_epoch: 0
Epoch 6/100
 - 83s - loss: 0.2851 - val_loss: 0.2177
2019-07-19 15:35:55,200 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6755, ER_overall: 0.4591, Best ER: inf, best_epoch: 0
Epoch 7/100
 - 83s - loss: 0.2660 - val_loss: 0.1822
2019-07-19 15:37:31,800 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8357, ER_overall: 0.2612, Best ER: inf, best_epoch: 0
Epoch 8/100
 - 82s - loss: 0.2765 - val_loss: 0.2440
2019-07-19 15:39:06,306 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6129, ER_overall: 0.5379, Best ER: inf, best_epoch: 0
Epoch 9/100
 - 83s - loss: 0.2367 - val_loss: 0.1830
2019-07-19 15:40:43,491 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8168, ER_overall: 0.2459, Best ER: inf, best_epoch: 0
Epoch 10/100
 - 82s - loss: 0.2182 - val_loss: 0.1346
2019-07-19 15:42:19,051 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.887, ER_overall: 0.1528, Best ER: inf, best_epoch: 0
Epoch 11/100
 - 83s - loss: 0.2403 - val_loss: 0.1610
2019-07-19 15:43:56,074 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8259, ER_overall: 0.2329, Best ER: inf, best_epoch: 0
Epoch 12/100
 - 84s - loss: 0.2318 - val_loss: 0.1309
2019-07-19 15:45:33,352 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8803, ER_overall: 0.177, Best ER: inf, best_epoch: 0
Epoch 13/100
 - 85s - loss: 0.2157 - val_loss: 0.1554
2019-07-19 15:47:11,884 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8479, ER_overall: 0.2142, Best ER: inf, best_epoch: 0
Epoch 14/100
 - 84s - loss: 0.2246 - val_loss: 0.1903
2019-07-19 15:48:49,284 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7801, ER_overall: 0.2691, Best ER: inf, best_epoch: 0
Epoch 15/100
 - 83s - loss: 0.2092 - val_loss: 0.1896
2019-07-19 15:50:25,952 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7836, ER_overall: 0.2546, Best ER: inf, best_epoch: 0
Epoch 16/100
 - 86s - loss: 0.1870 - val_loss: 0.1712
2019-07-19 15:52:05,579 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8145, ER_overall: 0.2134, Best ER: inf, best_epoch: 0
Epoch 17/100
 - 83s - loss: 0.2136 - val_loss: 0.1748
2019-07-19 15:53:42,068 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7987, ER_overall: 0.2402, Best ER: inf, best_epoch: 0
Epoch 18/100
 - 82s - loss: 0.2037 - val_loss: 0.1394
2019-07-19 15:55:17,710 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8325, ER_overall: 0.2163, Best ER: inf, best_epoch: 0
Epoch 19/100
 - 83s - loss: 0.1746 - val_loss: 0.1580
2019-07-19 15:56:53,833 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8046, ER_overall: 0.2455, Best ER: inf, best_epoch: 0
Epoch 20/100
 - 83s - loss: 0.1710 - val_loss: 0.1484
2019-07-19 15:58:31,080 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8454, ER_overall: 0.1878, Best ER: inf, best_epoch: 0
Epoch 21/100
 - 83s - loss: 0.1894 - val_loss: 0.1362
2019-07-19 16:00:07,655 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8462, ER_overall: 0.2004, Best ER: inf, best_epoch: 0
Epoch 22/100
 - 82s - loss: 0.1999 - val_loss: 0.3001
2019-07-19 16:01:42,795 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.5921, ER_overall: 0.4569, Best ER: inf, best_epoch: 0
Epoch 23/100
 - 84s - loss: 0.1867 - val_loss: 0.1693
2019-07-19 16:03:19,593 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8061, ER_overall: 0.2371, Best ER: inf, best_epoch: 0
Epoch 24/100
 - 84s - loss: 0.1731 - val_loss: 0.1310
2019-07-19 16:04:57,603 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8498, ER_overall: 0.1758, Best ER: inf, best_epoch: 0
Epoch 25/100
 - 83s - loss: 0.1682 - val_loss: 0.1630
2019-07-19 16:06:34,105 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8046, ER_overall: 0.2449, Best ER: inf, best_epoch: 0
Epoch 26/100
 - 85s - loss: 0.1927 - val_loss: 0.1779
2019-07-19 16:08:11,867 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7794, ER_overall: 0.2713, Best ER: inf, best_epoch: 0
Epoch 27/100
 - 82s - loss: 0.2237 - val_loss: 0.1404
2019-07-19 16:09:47,171 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8391, ER_overall: 0.2072, Best ER: inf, best_epoch: 0
Epoch 28/100
 - 83s - loss: 0.1659 - val_loss: 0.1375
2019-07-19 16:11:23,966 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8599, ER_overall: 0.1736, Best ER: inf, best_epoch: 0
Epoch 29/100
 - 82s - loss: 0.1922 - val_loss: 0.1519
2019-07-19 16:12:59,175 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.826, ER_overall: 0.2267, Best ER: inf, best_epoch: 0
Epoch 30/100
 - 82s - loss: 0.1880 - val_loss: 0.1718
2019-07-19 16:14:34,400 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7827, ER_overall: 0.2547, Best ER: inf, best_epoch: 0
Epoch 31/100
 - 83s - loss: 0.1632 - val_loss: 0.1204
2019-07-19 16:16:12,067 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8727, ER_overall: 0.1596, Best ER: inf, best_epoch: 0
Epoch 32/100
 - 82s - loss: 0.2018 - val_loss: 0.1105
2019-07-19 16:17:47,483 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8883, ER_overall: 0.1486, Best ER: inf, best_epoch: 0
Epoch 33/100
 - 82s - loss: 0.1810 - val_loss: 0.1463
2019-07-19 16:19:23,083 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8237, ER_overall: 0.22, Best ER: inf, best_epoch: 0
Epoch 34/100
 - 81s - loss: 0.1971 - val_loss: 0.1084
2019-07-19 16:20:57,930 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.892, ER_overall: 0.1344, Best ER: inf, best_epoch: 0
Epoch 35/100
 - 81s - loss: 0.1885 - val_loss: 0.1558
2019-07-19 16:22:32,366 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8186, ER_overall: 0.2144, Best ER: inf, best_epoch: 0
Epoch 36/100
 - 82s - loss: 0.1972 - val_loss: 0.1116
2019-07-19 16:24:07,252 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8996, ER_overall: 0.1395, Best ER: inf, best_epoch: 0
Epoch 37/100
 - 80s - loss: 0.1621 - val_loss: 0.2646
2019-07-19 16:25:40,878 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6697, ER_overall: 0.3752, Best ER: inf, best_epoch: 0
Epoch 38/100
 - 83s - loss: 0.1650 - val_loss: 0.1226
2019-07-19 16:27:17,262 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8834, ER_overall: 0.1598, Best ER: inf, best_epoch: 0
Epoch 39/100
 - 83s - loss: 0.1905 - val_loss: 0.1395
2019-07-19 16:28:52,797 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.841, ER_overall: 0.2028, Best ER: inf, best_epoch: 0
Epoch 40/100
 - 84s - loss: 0.1801 - val_loss: 0.2048
2019-07-19 16:30:29,880 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7232, ER_overall: 0.3378, Best ER: inf, best_epoch: 0
Epoch 41/100
 - 83s - loss: 0.1842 - val_loss: 0.1327
2019-07-19 16:32:05,930 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8553, ER_overall: 0.1893, Best ER: inf, best_epoch: 0
Epoch 42/100
 - 81s - loss: 0.1707 - val_loss: 0.1969
2019-07-19 16:33:40,486 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7424, ER_overall: 0.3111, Best ER: inf, best_epoch: 0
Epoch 43/100
 - 84s - loss: 0.1682 - val_loss: 0.2378
2019-07-19 16:35:18,367 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6861, ER_overall: 0.3627, Best ER: inf, best_epoch: 0
Epoch 44/100
 - 82s - loss: 0.1773 - val_loss: 0.1910
2019-07-19 16:36:53,901 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.751, ER_overall: 0.2985, Best ER: inf, best_epoch: 0
Epoch 45/100
 - 82s - loss: 0.1673 - val_loss: 0.1451
2019-07-19 16:38:30,419 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8387, ER_overall: 0.1993, Best ER: inf, best_epoch: 0
Epoch 46/100
 - 81s - loss: 0.1690 - val_loss: 0.1527
2019-07-19 16:40:04,236 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8332, ER_overall: 0.2109, Best ER: inf, best_epoch: 0
Epoch 47/100
 - 81s - loss: 0.1599 - val_loss: 0.1612
2019-07-19 16:41:39,130 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8114, ER_overall: 0.2332, Best ER: inf, best_epoch: 0
Epoch 48/100
 - 81s - loss: 0.1604 - val_loss: 0.2577
2019-07-19 16:43:14,107 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.6645, ER_overall: 0.3881, Best ER: inf, best_epoch: 0
Epoch 49/100
 - 82s - loss: 0.1924 - val_loss: 0.1064
2019-07-19 16:44:49,746 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9014, ER_overall: 0.1269, Best ER: inf, best_epoch: 0
Epoch 50/100
 - 80s - loss: 0.1767 - val_loss: 0.1762
2019-07-19 16:46:23,750 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7974, ER_overall: 0.2484, Best ER: inf, best_epoch: 0
Epoch 51/100
 - 82s - loss: 0.1637 - val_loss: 0.1748
2019-07-19 16:47:59,036 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7854, ER_overall: 0.2723, Best ER: inf, best_epoch: 0
Epoch 52/100
 - 82s - loss: 0.1552 - val_loss: 0.0938
2019-07-19 16:49:34,282 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9145, ER_overall: 0.109, Best ER: inf, best_epoch: 0
Epoch 53/100
 - 80s - loss: 0.1639 - val_loss: 0.1887
2019-07-19 16:51:08,075 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7838, ER_overall: 0.2544, Best ER: inf, best_epoch: 0
Epoch 54/100
 - 81s - loss: 0.1558 - val_loss: 0.1359
2019-07-19 16:52:41,959 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8628, ER_overall: 0.1679, Best ER: inf, best_epoch: 0
Epoch 55/100
 - 81s - loss: 0.1532 - val_loss: 0.2086
2019-07-19 16:54:16,774 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7368, ER_overall: 0.3171, Best ER: inf, best_epoch: 0
Epoch 56/100
 - 82s - loss: 0.1638 - val_loss: 0.1453
2019-07-19 16:55:51,459 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8342, ER_overall: 0.2144, Best ER: inf, best_epoch: 0
Epoch 57/100
 - 81s - loss: 0.1413 - val_loss: 0.0961
2019-07-19 16:57:25,181 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9017, ER_overall: 0.1247, Best ER: inf, best_epoch: 0
Epoch 58/100
 - 80s - loss: 0.1223 - val_loss: 0.1175
2019-07-19 16:58:57,978 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8889, ER_overall: 0.1331, Best ER: inf, best_epoch: 0
Epoch 59/100
 - 83s - loss: 0.1645 - val_loss: 0.1436
2019-07-19 17:00:34,539 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8385, ER_overall: 0.2021, Best ER: inf, best_epoch: 0
Epoch 60/100
 - 81s - loss: 0.1307 - val_loss: 0.2301
2019-07-19 17:02:09,031 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7143, ER_overall: 0.3266, Best ER: inf, best_epoch: 0
Epoch 61/100
 - 81s - loss: 0.1421 - val_loss: 0.1398
2019-07-19 17:03:43,991 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8557, ER_overall: 0.1789, Best ER: inf, best_epoch: 0
Epoch 62/100
 - 82s - loss: 0.1472 - val_loss: 0.1346
2019-07-19 17:05:19,790 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.858, ER_overall: 0.1664, Best ER: inf, best_epoch: 0
Epoch 63/100
 - 82s - loss: 0.1686 - val_loss: 0.1879
2019-07-19 17:06:54,406 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7691, ER_overall: 0.2737, Best ER: inf, best_epoch: 0
Epoch 64/100
 - 80s - loss: 0.1561 - val_loss: 0.1165
2019-07-19 17:08:27,308 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8953, ER_overall: 0.1224, Best ER: inf, best_epoch: 0
Epoch 65/100
 - 81s - loss: 0.1466 - val_loss: 0.0985
2019-07-19 17:10:01,069 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9199, ER_overall: 0.0967, Best ER: inf, best_epoch: 0
Epoch 66/100
 - 82s - loss: 0.1506 - val_loss: 0.0992
2019-07-19 17:11:36,615 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9052, ER_overall: 0.1162, Best ER: inf, best_epoch: 0
Epoch 67/100
 - 82s - loss: 0.1566 - val_loss: 0.1914
2019-07-19 17:13:11,439 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8043, ER_overall: 0.2254, Best ER: inf, best_epoch: 0
Epoch 68/100
 - 81s - loss: 0.1633 - val_loss: 0.2336
2019-07-19 17:14:45,624 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7267, ER_overall: 0.3184, Best ER: inf, best_epoch: 0
Epoch 69/100
 - 79s - loss: 0.1306 - val_loss: 0.0928
2019-07-19 17:16:18,237 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9117, ER_overall: 0.1105, Best ER: inf, best_epoch: 0
Epoch 70/100
 - 82s - loss: 0.1271 - val_loss: 0.1518
2019-07-19 17:17:54,745 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8547, ER_overall: 0.1812, Best ER: inf, best_epoch: 0
Epoch 71/100
 - 80s - loss: 0.1480 - val_loss: 0.1292
2019-07-19 17:19:28,041 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8719, ER_overall: 0.1541, Best ER: inf, best_epoch: 0
Epoch 72/100
 - 82s - loss: 0.1296 - val_loss: 0.1884
2019-07-19 17:21:03,183 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7637, ER_overall: 0.2839, Best ER: inf, best_epoch: 0
Epoch 73/100
 - 83s - loss: 0.1475 - val_loss: 0.1919
2019-07-19 17:22:39,780 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7856, ER_overall: 0.2574, Best ER: inf, best_epoch: 0
Epoch 74/100
 - 82s - loss: 0.1395 - val_loss: 0.1093
2019-07-19 17:24:15,288 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8602, ER_overall: 0.1774, Best ER: inf, best_epoch: 0
Epoch 75/100
 - 82s - loss: 0.1367 - val_loss: 0.1450
2019-07-19 17:25:50,927 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8463, ER_overall: 0.1742, Best ER: inf, best_epoch: 0
Epoch 76/100
 - 83s - loss: 0.1562 - val_loss: 0.1670
2019-07-19 17:27:27,049 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8062, ER_overall: 0.2264, Best ER: inf, best_epoch: 0
Epoch 77/100
 - 82s - loss: 0.1371 - val_loss: 0.1239
2019-07-19 17:29:02,357 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8678, ER_overall: 0.166, Best ER: inf, best_epoch: 0
Epoch 78/100
 - 81s - loss: 0.1231 - val_loss: 0.1518
2019-07-19 17:30:36,908 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8203, ER_overall: 0.2264, Best ER: inf, best_epoch: 0
Epoch 79/100
 - 81s - loss: 0.1054 - val_loss: 0.1022
2019-07-19 17:32:11,410 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9078, ER_overall: 0.1175, Best ER: inf, best_epoch: 0
Epoch 80/100
 - 81s - loss: 0.1071 - val_loss: 0.1093
2019-07-19 17:33:46,203 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9114, ER_overall: 0.1124, Best ER: inf, best_epoch: 0
Epoch 81/100
 - 84s - loss: 0.1737 - val_loss: 0.2362
2019-07-19 17:35:23,754 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.703, ER_overall: 0.3453, Best ER: 0.3453, best_epoch: 80
Epoch 82/100
 - 86s - loss: 0.1437 - val_loss: 0.1530
2019-07-19 17:37:02,901 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8399, ER_overall: 0.1984, Best ER: 0.1984, best_epoch: 81
Epoch 83/100
 - 87s - loss: 0.1429 - val_loss: 0.0913
2019-07-19 17:38:42,856 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.9172, ER_overall: 0.112, Best ER: 0.112, best_epoch: 82
Epoch 84/100
 - 84s - loss: 0.1249 - val_loss: 0.1294
2019-07-19 17:40:19,533 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8703, ER_overall: 0.1729, Best ER: 0.112, best_epoch: 82
Epoch 85/100
 - 81s - loss: 0.1363 - val_loss: 0.0901
2019-07-19 17:41:54,386 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.8999, ER_overall: 0.126, Best ER: 0.112, best_epoch: 82
Epoch 86/100
 - 81s - loss: 0.1766 - val_loss: 0.1776
2019-07-19 17:43:29,291 - model_base.py:94- INFO - LR: 0.001, F1_overall: 0.7857, ER_overall: 0.2541, Best ER: 0.112, best_epoch: 82
Epoch 87/100
 - 82s - loss: 0.1537 - val_loss: 0.0813
2019-07-19 17:45:04,114 - model_base.py:94- INFO - LR: 0.0008, F1_overall: 0.9177, ER_overall: 0.104, Best ER: 0.104, best_epoch: 86
Epoch 88/100
 - 82s - loss: 0.1382 - val_loss: 0.1155
2019-07-19 17:46:40,249 - model_base.py:94- INFO - LR: 0.0008, F1_overall: 0.877, ER_overall: 0.1533, Best ER: 0.104, best_epoch: 86
Epoch 89/100
 - 84s - loss: 0.1444 - val_loss: 0.1837
2019-07-19 17:48:17,644 - model_base.py:94- INFO - LR: 0.0008, F1_overall: 0.7845, ER_overall: 0.2537, Best ER: 0.104, best_epoch: 86
Epoch 90/100
 - 82s - loss: 0.1274 - val_loss: 0.1111
2019-07-19 17:49:52,815 - model_base.py:94- INFO - LR: 0.0008, F1_overall: 0.895, ER_overall: 0.1378, Best ER: 0.104, best_epoch: 86
Epoch 91/100
 - 83s - loss: 0.1481 - val_loss: 0.1169
2019-07-19 17:51:29,156 - model_base.py:94- INFO - LR: 0.00064, F1_overall: 0.8736, ER_overall: 0.1545, Best ER: 0.104, best_epoch: 86
Epoch 92/100
 - 80s - loss: 0.1423 - val_loss: 0.1177
2019-07-19 17:53:02,031 - model_base.py:94- INFO - LR: 0.00064, F1_overall: 0.8758, ER_overall: 0.1566, Best ER: 0.104, best_epoch: 86
Epoch 93/100
 - 80s - loss: 0.1490 - val_loss: 0.1344
2019-07-19 17:54:35,484 - model_base.py:94- INFO - LR: 0.000512, F1_overall: 0.8493, ER_overall: 0.1808, Best ER: 0.104, best_epoch: 86
Epoch 94/100
 - 83s - loss: 0.1617 - val_loss: 0.1458
2019-07-19 17:56:11,909 - model_base.py:94- INFO - LR: 0.000512, F1_overall: 0.8139, ER_overall: 0.2216, Best ER: 0.104, best_epoch: 86
Epoch 95/100
 - 84s - loss: 0.1539 - val_loss: 0.1541
2019-07-19 17:57:50,099 - model_base.py:94- INFO - LR: 0.0004096, F1_overall: 0.8167, ER_overall: 0.2164, Best ER: 0.104, best_epoch: 86
Epoch 96/100
 - 86s - loss: 0.1232 - val_loss: 0.1318
2019-07-19 17:59:28,878 - model_base.py:94- INFO - LR: 0.0004096, F1_overall: 0.8519, ER_overall: 0.1771, Best ER: 0.104, best_epoch: 86
Epoch 97/100
 - 83s - loss: 0.1360 - val_loss: 0.1141
2019-07-19 18:01:05,499 - model_base.py:94- INFO - LR: 0.00032768, F1_overall: 0.8651, ER_overall: 0.1624, Best ER: 0.104, best_epoch: 86
Epoch 98/100
 - 85s - loss: 0.1227 - val_loss: 0.1181
2019-07-19 18:02:43,130 - model_base.py:94- INFO - LR: 0.00032768, F1_overall: 0.8731, ER_overall: 0.161, Best ER: 0.104, best_epoch: 86
Epoch 99/100
 - 82s - loss: 0.1163 - val_loss: 0.1268
2019-07-19 18:04:18,885 - model_base.py:94- INFO - LR: 0.000262144, F1_overall: 0.8578, ER_overall: 0.1737, Best ER: 0.104, best_epoch: 86
Epoch 100/100
 - 82s - loss: 0.1352 - val_loss: 0.1231
2019-07-19 18:05:53,982 - model_base.py:94- INFO - LR: 0.000262144, F1_overall: 0.8716, ER_overall: 0.1616, Best ER: 0.104, best_epoch: 86
2019-07-19 18:05:54,215 - BBC.3.2.py:164- INFO - Start decoding
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/home/xiaorong/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1145: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
[[  60    0    0    0    0    0    1]
 [   0   11    0    0    0    6    1]
 [  28   55 1435    3   13   38   96]
 [   0    1   10  457    0    2    5]
 [   0    0    0    0    9    0    0]
 [   0    1    0    2    0   18    3]
 [   0    0    0    0    0    0    0]]
              precision    recall  f1-score   support

       Siren       0.68      0.98      0.81        61
     Thunder       0.16      0.61      0.26        18
         Car       0.99      0.86      0.92      1668
        Foot       0.99      0.96      0.98       475
        Fire       0.41      1.00      0.58         9
        Door       0.28      0.75      0.41        24
     unknown       0.00      0.00      0.00         0

   micro avg       0.88      0.88      0.88      2255
   macro avg       0.50      0.74      0.56      2255
weighted avg       0.97      0.88      0.92      2255

2019-07-19 20:03:01,136 - BBC.3.2.py:215- INFO - Test utt-level acc: 0.882483
2019-07-19 20:03:01,136 - BBC.3.2.py:216- INFO - End decoding
